用跨域数据训练通用特征提取器，提升小样本下图像重建质量
Transferable Optimization Network for Cross-Domain Image Reconstruction
- 分两步优化：先学通用特征，再针对新任务微调适配器
- 仅用少量数据即可实现高质量欠采样MRI重建
- 适合数据稀缺场景，如医疗影像、低资源图像恢复
我们提出一种新型迁移学习框架，解决图像重建中训练数据有限的问题。该框架包含两个步骤，均为双层优化形式。第一步训练一个强大的通用特征提取器，从不同领域的大规模异构数据中学习重要知识，包括其他解剖结构的图像、不同采样率的测量数据，以及自然图像等不同模态的数据。第二步在仅有少量数据的情况下，为新的目标域或任务训练一个特定领域的适配器。将适配器与通用特征提取器组合后，可有效挖掘对新领域图像正则化至关重要的特征，从而在数据受限情况下实现高质量重建。实验表明该方法在欠采样MRI重建中具有显著迁移能力。
原文摘要 · Abstract (English)
We develop a novel transfer learning framework to tackle the challenge of limited training data in image reconstruction problems. The proposed framework consists of two training steps, both of which are formed as bi-level optimizations. In the first step, we train a powerful universal feature-extractor that is capable of learning important knowledge from large, heterogeneous data sets in various domains. In the second step, we train a task-specific domain-adapter for a new target domain or task with only a limited amount of data available for training. Then the composition of the adapter and the universal feature-extractor effectively explores feature which serve as an important component of image regularization for the new domains, and this leads to high-quality reconstruction despite the data limitation issue. We apply this framework to reconstruct under-sampled MR images with limited data by using a collection of diverse data samples from different domains, such as images of other anatomies, measurements of various sampling ratios, and even different image modalities, including natural images. Experimental results demonstrate a promising transfer learning capability of the proposed method.
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